Research on intelligent test paper generation base on improved genetic algorithm

In order to solve the problems such as blindfold search, slower convergence, and sometimes unsuccessfully search in the present genetic algorithms used for intelligent test paper generation, this paper introduces an improved genetic algorithm for intelligent test paper generation. This algorithm generates optimized initial chromosome group and controlling crossing and variation recurring to test paper parameters and randomization. Consequently, it can avoid the problems of blind search and slower convergence resulted by complete randomization controlling adopted by the present genetic algorithms. At last, this paper proves the improved genetic algorithm can better solve the intelligent test paper generation problem compared to present genetic algorithms mentioned in algorithm analysis section of this paper.